The extraction of roads from high spatial resolution remote sensing images remains a problem though lots of efforts have
been made in this area. High spatial resolution remote sensing images represent the surface of the earth in detail. As
spatial resolution increases, spectral variability within the road cover units becomes complex and traditional remote
sensing image processing methods on pixel basis are no longer suitable. This paper studies automatic road extraction
from remote sensing images based on methods of Pulse-Coupled Neural Network and mathematical morphology. PCNN
is a useful biologically inspired algorithm, and has the properties of linking field and dynamic threshold which make
similar neurons generate pulses simultaneously. PCNN has the ability of a neuron to capture neighboring neurons which
are in similar states and the independency of the pulses within unattached neuron regions. The method of mathematical
morphology has the prime principle which is using a certain structure element to measure and extract the corresponding
form in an image. In this paper, the simplified PCNN is applied as the image segmentation algorithm, and morphological
transformation is used to purify the roads' information and to extract the road centerlines. Experimental results show that
this method is efficient in road extraction from remote sensing images.
The automatic extraction of roads from high spatial resolution remote sensing images is the hotspot and difficulty in
remote sensing research, and has attracted extensive attention. Road extraction from remote sensing images is a leading
direction of remote sensing image processing and widely demanded in transportation, mapping, urban planning and other
fields. In this paper, the actuality of road extraction is investigated. The road extraction process is divided into five
phases, which are pre-processing, low-level processing, mid-level processing, high-level processing and the application
of extraction results. The methods used in each phase are analyzed. Low-level processing is considered to be the key and
foundation of road extraction. Based on the representative method in low-level processing, mathematical morphology,
this paper presents an approach for automatic road extraction and tests the method on remote sensing image. The
experimental result shows the efficiency of the presented approach.
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